A contrastive learning approach for enhanced robustness for strengthening federated intelligence in internet of visual things

Abstract

As the Internet of Things (IoT) continues to grow, edge intelligence has developed as a favorable paradigm to enable effective and instantaneous processing of data at the network's edge. The deployment of an edge intelligence approach in untrusted environments exposes them to adversarial attacks, posing a substantial challenge to the reliability and confidentiality of the learned models especially in federated settings. Among these attacks, the white-box attacks are the most dangerous and challenging to defend in IoT systems. This study proposes a novel approach to robustify the federated edge intelligence technique by integrating the power of contrastive learning to enhance the model's ability to distinguish between clean and adversarial examples, thereby improving its robustness. Our robustification approach is based on two lines of defense. First, an elegant federated contrastive pretraining is introduced to robustify the model by adversarially training it using a label-free representation attack. Second, we introduce a novel Consistency Regularized Triplet (CRT) loss function that encourages the regularization of the federated edge intelligence model to learn discriminative representations while minimizing the influence of adversarial perturbations. The empirical evaluation of the Food-101 and CIFAR-100 datasets revealed that the proposed method outperformed the state-of-the-art robust methods on both standard accuracy and robustness accuracy.